Impairment correlation estimation plays an important role in communication signal processing, because such estimation provides for the suppression of correlated interference. A primary example of correlation-based processing is the Generalized Rake (G-Rake) receiver, as generally described in Bottomley, Ottosson, and Wang, “A Generalized RAKE Receiver for Interference Suppression,” IEEE J. Sel. Areas Commun., vol. 18, No. 8 (August 2000). See, also, U.S. Pat. No. 7,397,842 to Bottomley, et al. Of course, the same or similar impairment correlation processing is used in equivalent receiver architectures, such as Chip Equalization (CE) architectures.
Several challenges arise in estimating impairment correlations. For example, the estimations should be relatively free of estimation noise. One approach to reducing estimation noise involves time filtering, where impairment correlation estimates are filtered over a number of signal intervals, and the filtered estimates are used for generating signal combining weights. While time filtering can be extended as needed to obtain a desired level of noise suppression, this approach undesirably masks underlying changes in the actual interference conditions at the receiver, where those changes ideally would be reflected in the impairment correlation estimates.
The ability to track changing interference conditions is becoming increasingly useful, because scheduled, high-power data transmissions in the newer communication networks, such as with HSPDA services in Wideband CDMA, produce significant changes in the type and source of received signal interference incurred by a given receiver over multiple transmission intervals. There are known approaches to producing correlation estimates that are more responsive to fast fading, and changing interference conditions.
For example, U.S. Pat. No. 7,486,716 to Fulghum et al. uses chip samples of a received signal for correlation estimation. This stands in contrast to the use of despread pilot values, e.g., despread Common Pilot Channel (CPICH) symbols, for impairment correlation estimation. The use of chip samples provides a much larger number of samples for correlation estimation over a given transmission interval, as compared to pilot symbols. With this approach, time filtering within a given signal interval can be more effective than time filtering within the interval using the much smaller set of pilot-derived correlations.
Parametric estimation represents another significant and extensively developed approach to the determination of impairment correlation estimations that track changing interference conditions, while exhibiting reduced estimation noise. In parametric estimation, rough, potentially noisy, measurements of actual impairment correlations are taken, but these measurements are not used for combining weight generation. Rather, they are used in a model fitting process, that uses least-squares or other processing to fit a parametric model of impairment correlations to the measurements. See, for example, U.S. Pub. 2007/0047628 A1, to Fulghum et al., which discloses parametric-based impairment correlation estimation in the context of Quadrature Amplitude Modulation (QAM) signal processing. Another parametric example is given in U.S. Pub. 2007/0253514, which recognizes the Toeplitz nature of a correlation-related matrix, based on the tap delay locations. This disclosure teaches the formation of a circulant matrix, which is special form of a Toeplitz matrix, and that specialized matrix is used for parametric estimation of impairment correlations.
Still further, it is known to perform both parametric and non-parametric processing within the same receiver, and to apply time filtering in this context, for improved impairment correlation estimation. See, for example, U.S. Pub. 2005/0215218 to Bottomley et al.